DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments, see page 8, filed 3/2/2026, with respect to specification objection have been fully considered and are persuasive. The objection of the specification has been withdrawn.
Applicant’s arguments, see page 8, filed 3/2/2026, with respect to 101 rejection have been fully considered and are persuasive. The 101 rejection of the claims has been withdrawn.
Applicant’s arguments, see pages 8-10, filed 3/2/2026, with respect to the rejection(s) of claim(s) 1-4, 7-12 and 15-20 under 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Petrey. In particular, the reference of Miwa is still applied to the filtering aspect of the claims. As stated in the Miwa reference ¶ [112]-[114], a face database is used to store registered and authorized individuals as well as persons who may be considered as unauthorized or who exhibit suspicious behavior. The system can collect a plurality of identifiers of persons at a database, but these identifiers can be filtered when a person who performs suspicious actions is detected and is performing suspicious behavior. The system detects the suspicious behavior associated with the person identified either as an intruder or as a suspicious person registered within the system. In ¶ [308]-[316] a person is identified as an intruder during a security incident at a specific time period (i.e. during an emergency and during various intervals). The intruder image and the location within a layout is output to local or emergency personal. These are examples of filtering data from all the users registered and stored to focus on a specific person associated with an incident at particular times. However, this reference is not specific in performing the feature of “retrieving, from the database, a set of object identifiers with time stamps within the time window and a matching location of the security event”. This is cured by the Petrey reference.
Regarding the Petrey reference, this system discloses storing identifiers of a user within an image with a time of an incident, location and type of incident within a cloud or server, which is taught in ¶ [81] and [83]. The system retrieves this information in order to input this data into a machine learning model, which is taught in ¶ [45], [46] and [61]. Based on detecting a certain user at a scene, information related to the person involved in the incident can be filtered in order to identify a probability of a further incident regarding the detected or identified person. This is explained in ¶ [90]-[93] and [99]. With the combination of this reference, this reference performs both the retrieving step and the filtering step. Thus, based on the combination of references, the features of the independent claims are performed.
Therefore, based on the above, the features of the claims are disclosed below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-4, 7-12 and 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Miwa (US Pub 2018/0308326) in view of Petrey (US Pub 2021/0019645).
Re claim 1: (Original) Miwa discloses a method for providing contextual data for a security event in an environment, comprising:
receiving sensor data from a plurality of sensors located in the environment, wherein the plurality of sensors comprises at least one camera and the sensor data includes at least a plurality of images captured by the at least one camera (e.g. the invention discloses several sensors that receive data within an area, wherein one of the sensors is a monitoring camera that can capture images of an area, which is taught in ¶ [62] and [69].);
[0061] [Safety-Related Device 200]
[0062] The safety-related device 200 comprises a Wi-Fi (Wireless Fidelity) terminal (hereinafter referred to as Wi-Fi slave device) 201, an RFID reader 202, an iBeacon slave device 203, a motion sensor 211, a laser radar 212, a monitoring camera 221, a monitor 222, a speaker 223, a microphone 224, a relay box 225, an electric lock 226, a fire alarm 227, a fire door/fire shutter 228, a circulator (hereinafter also referred to as air blower) 229, a floodlight 230, a repelling device 231, a mechanical smoke vent 232, a carbon oxide (hereinafter referred to as CO) detector 233, a plurality of smoke detectors 234 that detect smoke in case of fire, and an air blower 235 that diffuses smoke. An additional mechanical smoke vent 232 maybe installed at an arbitrary position between the mechanical smoke vents 232.
[0063] The iBeacon slave device 203 stands by in the background by executing an application with an iBeacon function, and excites a predetermined action when coming close to an iBeacon master device 302 described below of the portable device 300. The iBeacon slave device can detect position information of the iBeacon master device 302.
[0064] The motion sensor 211 is a sensor to detect a location of a human. It uses infrared rays, ultrasonic waves, and visible light, etc.
[0065] The laser radar 212 measures a size, position, and speed of an object, and detects intrusion and traverse of a suspicious person. The laser radar 212 is installed in each of important security sections such as an office, a design room, a research and development room, and a management room, etc. In addition, the laser radar is also installed at an entrance/exit of a building to which people do not usually enter.
[0066] The monitor 222 maybe a television screen or an LED display.
[0069] A part or all of the monitoring cameras 221 is a PTZ camera having a PTZ (pan/tilt/zoom) function, and is remotely operated by the monitoring device 100. The monitoring camera 221 is installed at each point in a monitoring target area such as a backyard facility as an area including a truck yard that those other than relevant persons are prohibited from entering, and shoots the monitoring target area. Also, the monitoring camera 221 is a camera for authentication to shoot a face of a person. An image shot by the monitoring camera 221 is output to the monitoring device 100. The monitoring camera 221 may always shoot a moving image or shoot a still image at regular intervals (for example, every several seconds).
parsing the sensor data, wherein the parsing comprises:
identifying a plurality of objects in each image of the plurality of images (e.g. the invention discloses identifying a suspicious person with the use of the monitoring camera captured device. The monitoring camera can recognize a face of an individual within the image. The recognition of a body and a face are a plurality of objects, which is taught in ¶ [139]-[142] and [146].); and
[0139] In Step S50, the control unit 110 causes the monitoring camera 221 that shoots the site of the suspicious person to pan, tilt, and zoom to shoot a close-up image of the suspicious person.
[0140] In Step S51, the control unit 110 picks-up a conversation between the suspicious person and a relevant person from the microphone 224 installed near the site of the suspicious person.
[0141] In Step S52, the control unit 110 transmits the taken-in image showing the status of the suspicious person (including a moving image) and the picked-up suspicious person's sound to the portable devices 300 of the relevant persons, security-related persons, and relevant authorities by e-mail. Other relevant persons can confirm the status of the suspicious person in real time, and prepare for responding to the site and accompanying caution. In addition, the status of the suspicious person can be accurately reported to the relevant authorities.
[0142] In Step S53, the control unit 110 displays an image and sound of the suspicious person on the monitors 222 and ends this flow. The image and sound of the suspicious person may be displayed on only the monitor 222 at the site of the incident (for example, on a corresponding floor).
[0145] In Step S61, the control unit 110 determines a suspicious person by using the laser radar 212. In an important security section such as an office , a design room, a research and development room, a management room, etc., the laser radar 212 that detects a suspicious person is installed. The laser radar 212 is also installed at an entrance/exit, etc., in a building that people do not usually enter or exit from. When the laser radar 212 operates , an intruder (suspicious person) is determined (definitely determined) without waiting for the following processing.
[0146] When the laser radar 212 does not operate, in Step S62, the control unit 110 determines a candidate for a suspicious person by face authentication. In detail, the control unit 110 takes-in a video of the monitoring camera 221. It is determined whether a facial image of a person shot by the monitoring camera 221 matches a facial image of a relevant person such as a facility relevant person, a person with a registered RFID authentication card, a relevant person, etc. , and if the result of determination is not a match, this person is determined as a candidate for a suspicious person, and the process advances to Step S66.
[0147] When the person is not a candidate for a suspicious person, in Step S63, the control unit 110 determines a candidate for a suspicious person by Wi-Fi authentication. In detail, in the following description, the Wi-Fi master device 301 that receives and individually identifies radio waves of the Wi-Fi slave devices 201 is registered for Wi-Fi authentication. In addition, the positions of the installed Wi-Fi slave devices 201 are also registered in the Wi-Fi master device 301. When a relevant person passes through a radio region of the Wi-Fi slave device 201, it receives a radio wave of the portable device of the relevant person and transmits a radio wave showing that the relevant person K3 is in the region of the Wi-Fi slave device 201 to the Wi-Fi master device. The control unit 110 receives this radio wave and determines whether this relevant person is an authorized person by collation. When this person is a candidate for a suspicious person, the process advances to Step S66.
[0148] When the person is determined as not being a candidate for a suspicious person through Wi-Fi authentication, in Step S64, the control unit 110 determines a candidate for a suspicious person by iBeacon authentication. In detail, in the work facility, iBeacons with a radio wave identification range of approximately 10 cm to 5 m are installed. In addition, in the monitoring device 100, portable devices of the relevant persons in the work facility are registered. When a relevant person passes through or performs work at a location where the iBeacon is positioned, the portable device of the relevant person receives a radio wave transmitted from the iBeacon. The portable device of the relevant person transmits a radio wave showing it is present at the position of the iBeacon to the monitoring device 100. The control unit 110 receives this radio wave and determines whether this relevant person is an authorized person by collation. When the person is a candidate for a suspicious person, the process advances to Step S66.
[0149] When the relevant person is determined as not being a candidate for a suspicious person through iBeacon authentication, in Step S65, the control unit 110 determines a candidate for a suspicious person by RIFD authentication. In detail, RFID readers 202 are installed in the work facility. RFID tags (authentication cards 31) are lent to relevant persons who enter and exit from the work facility and clients authorized to perform work. The lent RFID tag (authentication card 31) is permitted to enter and exit from facilities such as passageways, areas, storehouses, and work rooms in a permitted time (day). When the relevant person or client makes entry or exit or performs work within a radio-wave transmission distance of the RFID reader 202, the RFID tag of the portable device 300 of the relevant person or client receives a radio wave transmitted from the RFID reader 202. The control unit 110 receives a radio wave of this RFID tag, and determines whether the relevant person or client is an authorized relevant person or client by collation, and determines his/her position. When the person is a candidate fora suspicious person, the process advances to Step S66.
[0150] When the person is determined as a suspicious person through the RIFD authentication, the candidate fora suspicious person determined in Step S66 is identified as a suspicious person and the process returns to Step S41 shown in FIG. 4.
determining attributes of each object of the plurality of objects (e.g. the system can determine what an individual is saying, where the body is located, the facial features of the user and if the user is associated with an authorized person, which is taught in ¶ [139]-[142], [145], and [150] above.);
storing, in a database, the parsed sensor data comprising identifiers of the plurality of objects and the attributes (e.g. a database can store the name of a user who is permitted to use the system and enter the premises. The facial attribute of the permitted user is stored in the database with other detailed information and numbers associated with each user, which is taught in ¶ [111]-[114] and [293].),
[0111] FIG. 3A is a flowchart showing person registration processing of the control unit 110 of the monitoring device 100 of the digital smart safety system. This flow is executed by the control unit 110 of the monitoring device 100.
[0112] In Step S1, the control unit 110 registers face information of persons issued with RFID authentication cards, facility relevant persons, and relevant persons, etc., in the face information DB 160. In detail, the control unit 110 acquires, from face regions, information representing humans' facial characteristics (face information) to be used for face authentication, and registers the information in the face information DB 160 in association with the individual images. In addition, the control unit 110 receives persons' images transmitted from the headquarters (not shown) and registers the images in the face information DB 160.
[0113] In Step S2, the control unit 110 registers, in the face information DB 160, face information of persons other than persons who carry registered RFID tags (authentication cards 31) to be authenticated by the RFID readers 202. Persons other than the persons who carry the registered authentication cards 31 are suspicious persons or suspicious vehicle drivers. These suspicious persons also include persons who habitually perform suspicious behavior. The person who habitually performs suspicious behavior is, for example, a person who frequently appears at a site of theft or a person reported in advance as a person on a blacklist from headquarters /head office or a security company. In the present embodiment, a level of monitoring a person who habitually performs suspicious behavior is set to be higher. Registration of face information of persons may be updating of the face information DB 160 from headquarters/head office or a security company.
[0114] In Step S3, the control unit 110 registers detailed information, vehicle registration numbers, and related information of persons issued with authentication cards 31, facility relevant persons, and relevant persons, etc.
[0293] (1) iBeacon master devices 302 are numbered, and installed in work rooms, important departments, and at positions where relevant persons work in the facility in question. The monitoring device 100 stores a layout chart of the work facility in question, positions where all relevant persons K1 to K200 respectively work, and phone numbers of the portable devices 300 of the relevant persons K1 to K200 in the safety-related information storage DB 135.
wherein the database is structured such that object information is organized by timestamps and associated location in the environment (e.g. the database contains days and times when users are permitted to enter a specific area of the facility. It also identifies the users that are associated with positions in those areas, which is taught in ¶ [293] above, [248] and [249].);
[0248] The monitoring device 100 stores permitted areas, room names, days, and times as authorized content in the database.
[0249] An authorized relevant person R10 (not shown) can act and work in, for example, the whole facility and the whole public room such as the restroom, etc. An authorized relevant person R11 (not shown) can act and work at a time and on a day determined for work in work areas including the work room, the research and development room, the design room, the material room, and the customer information room, etc.
detecting the security event at the environment; determining a type, a time window, and a location of the security event (e.g. the invention can detect a fire or emergency event, determine the location, time frame with the use of an email and location of a nearby person to assist someone caught in the emergency event, which is taught in ¶ [216].);
[0216] In the second mode, after an elapse of 5 minutes from detection of the fire by the fire alarm, a customer or relevant person who has failed to escape is found. Most of the customers and relevant persons evacuate from the building facility, however, it is also assumed that some are involved in flame, smoke, toxic gases, etc., and fail to escape. In order to detect a customer or relevant person who has failed to escape, the monitoring device 100 collects signals of the respective sensors (RFIDs and Wi-Fi, etc.). That is, the monitoring device 100 does not determine a suspicious person but detects a person who has failed to escape by using the sensors. For example, people who are customers and other persons without registered RFID reader 202, portable device 300, and have failed to escape, are detected. For example, the Wi-Fi devices detect at which of the smoke detectors SM1 to SM10 in the blocks Z1 to Z250 of the building facility a person who has failed to escape is. In addition, the motion sensor 211 in the block Z30 detects that a person is at the motion sensor 211 in the block Z30. The monitoring device 100 transmits an e-mail, a layout, an image, and sound to the portable device 300 of a relevant person near Z30. The relevant person responds to the management personnel in Z30, and can rescue and evacuate the person who has failed to escape.
retrieving, from the database, a set of object identifiers and a matching location of the security event (e.g. a user registered within the system and associated with a RFID tag can be detected, which is a type of identifier. The person can be detected within the permitted time to work in a specific area that matches the security database positions. This information can be retrieved within a time window of when a user is authorized to work and an email with the timestamp and location of the specific area where the emergency is occurring can be retrieved, which is taught in ¶ [216], [248], [249] and [293] above);
filtering, from the set of object identifiers, at least one object identifier that matches the type of the security event (e.g. the system determines if persons are not recognized out of the stored persons within the face database (160). If the person is not recognized, the person is considered as a suspicious person. The person can also be detected by filtering images of people within a face database (160) and finding a person who performs suspicious behavior, which is taught in ¶ [112]-[114]. This person can be detected as a matching habitual suspicious behavior.); and
[0112] In Step S1, the control unit 110 registers face information of persons issued with RFID authentication cards, facility relevant persons, and relevant persons, etc., in the face information DB 160. In detail, the control unit 110 acquires, from face regions, information representing humans' facial characteristics (face information) to be used for face authentication, and registers the information in the face information DB 160 in association with the individual images. In addition, the control unit 110 receives persons' images transmitted from the headquarters (not shown) and registers the images in the face information DB 160.
[0113] In Step S2, the control unit 110 registers, in the face information DB 160, face information of persons other than persons who carry registered RFID tags (authentication cards 31) to be authenticated by the RFID readers 202. Persons other than the persons who carry the registered authentication cards 31 are suspicious persons or suspicious vehicle drivers. These suspicious persons also include persons who habitually perform suspicious behavior. The person who habitually performs suspicious behavior is, for example, a person who frequently appears at a site of theft or a person reported in advance as a person on a blacklist from headquarters /head office ora security company. In the present embodiment, a level of monitoring a person who habitually performs suspicious behavior is set to be higher. Registration of face information of persons may be updating of the face information DB 160 from headquarters/head office or a security company.
[0114] In Step S3, the control unit 110 registers detailed information, vehicle registration numbers, and related information of persons issued with authentication cards 31, facility relevant persons, and relevant persons, etc.
outputting, at a computing device, the at least one object identifier and attributes of the at least one object identifier in response to detecting the security event (e.g. the system can filter out the emergency situations from a suspicious person or a person who has failed to escape. Information used to identify this emergency is gathered and put into an email with the location of the person who is stuck in the emergency in order for emergency personal to rescue the individual in peril, which is taught in ¶ [104]-[106]).
[0104] In Step S106, based on the results of detection by the fire door detection means 113, the control unit 115 reports that a corresponding fire door/fire shutter 228 in an evacuation route is not closed to the facility relevant persons. This report may be made by any one of announcement, video, Wi-Fi/RFID of the relevant persons, etc. By receiving this report, the relevant persons can close the not closed fire door/fire shutter 228 after confirming the safety, so that a situation where the fire door/fire shutter 228 that should be closed is left not closed can be avoided. In addition, even in a case where danger is imminent and the fire door/fire shutter cannot be closed, the not closed state of the fire door/fire shutter 228 can be known in advance and then evacuation can be made safely.
[0105] In Step S107, the human detection means 114 (refer to FIG. 1) of the control unit 110 determines whether there is a person who has failed to escape in case of fire. To detect a customer or relevant person who has failed to escape, the monitoring device 100 collects signals of the respective sensors (RFIDs, Wi-Fi, etc.). That is, the monitoring device 100 does not determine a suspicious person but detects a person who has failed to escape by using the respective sensors. For example, a person who is a customer or other person and has not registered his/her RFID reader 202 or portable device 300 and has failed to escape is detected.
[0106] When there is a person who has failed to escape (Step S107: Yes), in Step S108, the control means 115 (refer to FIG. 1) of the control unit 110 makes a report to the person who has failed to escape based on the results of detection by the human detection means 114. The report to the person who has failed to escape is made by transmitting an e-mail, a layout, an image, or sound to the portable device 300 of a relevant person near the person who has failed to escape. The relevant person can respond to the report and rescue and evacuate the person who has failed to escape. When the speaker 223, etc., is present near the person who has failed to escape, the person who has failed to escape can be directly notified, and these methods may be used in combination. Accordingly, a communication channel to give an evacuation guidance to a person who has failed to escape increases , so that the effect of the evacuation guidance can be improved.
However, Miwa fails to specifically teach the features of retrieving, from the database, a set of object identifiers with timestamps within the time window and a matching location of the security event.
However, this is well known in the art as evidenced by Petrey. Similar to the primary reference, Petrey discloses acquiring information about people and incidents (same field of endeavor or reasonably pertinent to the problem).
Petrey discloses retrieving, from the database, a set of object identifiers with timestamps within the time window and a matching location of the security event (e.g. the invention discloses retrieving a base data set of information that is associated with an identifier of a person at the incident, the location and time of the incident that is associated with a particular incident, which is taught in ¶ [81] and [83]. This stored information is gathered from a cloud or server to be input into a machine learning model, which is taught in ¶ [45], [46] and [61]. The retrieving of the Id associated with a user involved in an incident, the time stamp of the incident, the type of incident and location is performed in order to be used by a machine learning model, which is taught in ¶ [90]-[93]. A person being at a prohibited location can be considered as an incident that is notified, which is taught in ¶ [99].).
[0045] The personal identification database 119 may be populated by a processing device adding personal identification information associated with electronic device IDs 133 of electronic devices carried by people that commonly enter the electronic device detection zone 132 (e.g., a list of trusted electronic device IDs). In some embodiments, the personal identification database 119 may be populated at least in part by manual entry of personal identification information associated with electronic device IDs 133 associated with electronic devices 140 trusted to be within the electronic device detection zone 132 (e.g., a list of trusted electronic device IDs). These electronic device IDs 133 may be associated with electronic devices 140 owned by neighbors in a neighborhood, or family members of the neighbors, friends of the neighbors, visitors of the neighbors, contractors hired by the neighbors, etc. Further, in some embodiments, the personal identification database 119 may be populated by entering a list of known suspect individuals from the police department, people entering or exiting border checkpoints, etc.
[0046] The personal identification information for untrusted electronic device IDs may also be entered into the personal identification database 119. The personal identification database 119 may also be populated by a processing device adding personal identification information associated with electronic device IDs 133 of electronic devices carried by people that commonly enter the facial detection zone 132 (e.g., face images of trusted individuals). The personal identification information may include names, addresses, faces, email addresses, phone numbers, electronic device identifiers associated with electronic devices owned by the people (e.g., Bluetooth MAC addresses, WiFi MAC addresses), correlated license plate IDs with the electronic device identifiers, etc. The correlations between the license plate IDs, the electronic device identifiers, and/or the faces may be performed by a processing device using the data obtained from the cameras 120 and the electronic device identification sensors 130. Some of this information may be obtained from public sources, phone books, the Internet, and/or companies that distribute electronic devices. In some embodiments, the personal identification information added to the personal identification database 119 may be associated with people selected based on their residing in or near a certain radius of a geographic region where the zones 122 and/or 132 are set up, based on whether they are on a crime watch list, or the like.
[0061] At block 312, the license plate ID of the vehicle 126 may be correlated with at least one of the set of stored electronic device identifiers 133. In some embodiments, the face of the individual 142 may also be correlated with the license plate ID and the at least one of the set of stored electronic device identifiers 133. In some embodiments, at least one personal identification database 119 may be accessed. In some embodiments, correlating the license plate ID of the vehicle 126 with at least one of the set of stored electronic device identifiers 133 may include comparing one or more time stamps of the set of captured images 123 with one or more time stamps of the set of stored electronic device identifiers 133. In some embodiments, correlating the license plate ID of the vehicle 126 with the at least one of the set of stored electronic device identifiers 133 may include analyzing at least one of (i) at least one strength of signal associated with at least one of the set of stored electronic device identifiers 133, and (ii) at least one visually estimated distance of at least one vehicle associated with at least one of the set of stored images 123.
[0081] In some embodiments the cloud-based computing system 116 may include a training engine 174 capable of generating the one or more machine learning models 172. The machine learning models 172 may be trained to determine a probability of occurrence of a subsequent event based on one or more identifiers of one or more people, information pertaining to an incident that occurred at a location where the one or more people were present at a particular time, additional information (e.g., criminal record, mugshot, electronic medical record, etc.) pertaining to the one or more people, or some combination thereof. Further, the one or more machine learning models 172 may be trained to determine a preventative action to select and perform based on a severity of a subsequent incident that is determined occur and/or a probability of occurrence of the subsequent incident. For example, the machine learning model may be trained using training data that indicates certain preventative actions have higher success rates of reducing a probability that the subsequent event occurs. discover, translate, design, generate, create, develop, classify, and/or test candidate drug compounds, among other things. The one or more machine learning models 172 may be generated by the training engine 174 and may be implemented in computer instructions executable by one or more processing devices of the artificial intelligence engine 170, the training engine 174, and/or the servers 118. To generate the one or more machine learning models 172, the training engine 174 may train the one or more machine learning models 172.
[0083] To generate the one or more machine learning models 172, the training engine 174 may train the one or more machine learning models 172. The training engine 174 may use a base data set of a set of identifiers (e.g., electronic device IDs associated with the people, license plate numbers of the vehicles registered to the people, images of the people, etc.) of people that were present at a location an incident occurred at a particular time, information (e.g., type of incident, location of incident, time of incident, criminal or civil, damage to property, harm to people, etc.) pertaining to the incident, additional information pertaining to the people (e.g., one or more criminal records of the people, one or more mugshots of the people, addresses of the people, electronic medical records of the people, fingerprints of the people, images of the people, ages of the people, names of the people, email address associated with the people, phone numbers associated with the people, indications of the people being on a watch list, or some combination thereof) present at the incident at the location at the particular time, and/or a pattern recognized using the one or more identifiers, the information pertaining to the incident, or some combination thereof. The machine learning models 172 may be trained to receive, as input, subsequent identifiers associated with a person, information about incidents, and/or additional information pertaining to the person, and output a probability of occurrence of a subsequent incident. For example, if an identifier of a person is detected as being present at a location where a riot occurs (e.g., incident) with a certain threshold of other identifiers of other people also present at the location where the riot occurs, there may be a correlation between those identifiers and incidents occurring. Accordingly, if the identifier of the person is detected the next night at another location where the other identifiers of the other people are also detected, the probability of a subsequent incident occurring may be high. In other words, the people may be working together to initiate and/or instigate riots. Thus, using the trained machine learning models 172, subsequent incidents may be prevented.
[0090] In some embodiments, the cloud-based computing system 116 may receive information pertaining to the incident that occurred at the location where the person 142 was present at the first time. The information pertaining to the incident may include a description of the incident that occurred, a type of incident (e.g., criminal, civil, riot, vandalism, looting, drug trafficking, human trafficking, loitering, etc.), a timestamp of the incident, a duration of the incident, a location of the incident, and the like. The identifiers of the people and the information of the incident may be correlated and stored in database 119 and/or 117 for use by the trained machine learning models 123 to continuously update their determinations of probabilities of occurrences of subsequent incidents.
[0091] As depicted, a computing device 702 may provide the information pertaining to the incident to the cloud-based computing system 116 and/or additional information pertaining to the people present at the location of the incident at time T1. The computing device 702 may be associated with any suitable source that provides information pertaining to incidents that occur. For example, the computing device 702 may be associated with a law enforcement agency that provides police reports generated as a result of the incident, mugshots of people detected as being present at the location during the incident, criminal records of people detected as being present at the location during the incident, and so forth. The criminal record may indicate that a person is a 3 time convicted felon for armed robbery, and a machine learning model 172 may be trained to output a high probability of occurrence of a subsequent incident at a subsequent time when the person is detected if the person was recently detected as being present at a location where an armed robbery occurred. The computing device 702 may be associated with a healthcare facility that uses an electronic medical record (EMR) system. The EMR system may transmit information (e.g., medical records) of people to the cloud-based computing system 116 for use when determining the probability of occurrence of subsequent incidents. For example, the medical record may provide an indication if the person has been diagnosed as having a mental health related condition, which may be a relevant factor when determining whether a subsequent incident may occur. The computing device 702 may be associated with a news broadcasting entity, a social network entity, a social media entity, or the like.
[0092] The machine learning model 172 may receive the one or more identifiers (e.g., electronic device IDs 133) associated with the people present at the incident at the first time, the information pertaining to the incident, and/or the additional information pertaining to the people. In some embodiments, the cloud-based computing system 116 may correlate the identifiers, information pertaining to the incident, and/or the additional information pertaining to the people and store the correlated data in a database. In some embodiments, the machine learning models may be trained to determine probabilities of occurrences of subsequent incidents using the correlated data (e.g., the identifiers associated with the people, information pertaining to the incident, additional information pertaining to the people, etc.).
[0093] At time T2 (e.g., 9 PM January 2.sup.nd), subsequent to time T1 but prior to a subsequent incident occurring, the identifier of the electronic device 140 may be detected in the electronic device detection zone 132-2 again. In some embodiments, there may be a threshold number of the same identifiers detected in the electronic device detection zone 132-2 as were detected the night before when the incident occurred at time T1. The identifier may be transmitted to the cloud-based computing system 116, along with information pertaining to the location (e.g., GPS coordinates, etc.). The cloud-based computing system 116 may use the identifier to obtain any additional data about the people (e.g., license plate numbers of vehicles registered to the people, criminal records of the people, mugshots of the people, medical records of the people, etc.). In some embodiments, the AI engine 170 may input the identifiers of the people, the information pertaining to the location where the electronic devices 140 are located at time T2, and/or the additional information pertaining to the people into the machine learning model 172. The trained machine learning model may be receive the input and output a probability of occurrence of a subsequent incident 703. The probability of occurrence of a subsequent incident 703 may be a value, a percentage, a number, or the like.
[0099] In some embodiments, the incident may include the person being present at the location, which may trigger a preventative action to occur. For example, if a person is on a ban list and not allowed on a certain property, their detected presence at the location of the certain property may cause the person to be kicked off the property by security. In other examples, if a first person has a restraining order against a second person and both locations of the first and second person are detected within a certain distance prohibited by the restraining order, a preventative action may be performed (e.g., notify the electronic device of the second person to honor the restraining order and move farther away from the first person, notify the electronic device of the person that the second person is within the prohibited distance defined by the restraining order, notify law enforcement agency, etc.). In another example, if a person is on a wanted list, and their identifier is detected at a certain location, emergency services (e.g., law enforcement agency) may be notified and dispatched to the location to arrest the wanted person. In such examples, the identifier of the person may be received once from a particular location, and the detection of the person at the particular location may cause a preventative action to be performed. In such embodiments, it should be noted that any of the preventative actions described herein may be performed when the identifier of the person is detected at a certain location.
Therefore, in view of Petrey, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of retrieving, from the database, a set of object identifiers with timestamps within the time window and a matching location of the security event, incorporated in the device of Miwa, in order to retrieve information on individuals involved in an incident at a certain time or timeframe and location to report such persons, which aids in public safety of areas using the present technology (as stated in Petrey ¶ [26]).
Re claim 2: (Original) Miwa discloses the method of claim 1, wherein an object is a person and attributes of the person include a gender, an estimated age, biometrics, clothing information, and movement history (e.g. a facial attribute can be determined, which is taught in ¶ [111]-[114] above).
Re claim 3: (Original) Miwa discloses the method of claim 1, wherein detecting the security event further comprises determining that sensor data received from at least one sensor of the plurality of sensors comprises an abnormality (e.g. smoke can be detected by the smoke detectors to detect a fire, which is taught in ¶ [95]-[97].).
[0095] FIG. 2 is a flowchart showing fire guidance control of the control unit 110 of the monitoring device 100 of the digital smart safety system. This flow is repeatedly executed at predetermined timings (in units of ms) by the CPU constituting the control unit 110 (refer to FIG. 1). In consideration of urgency and importance of the fire guidance control, the fire guidance control is performed in priority to suspicious person/intruder determination control described below.
[0096] First, in Step S101, the control unit 110 acquires smoke information (whether smoke has been detected and smoke volume) from the plurality of smoke detectors 234 installed at various points in the facility.
[0097] In Step S102, the smoke determination means 112 (refer to FIG. 1) of the control unit 110 determines whether any one of the smoke detectors 234 has detected smoke. When no smoke is detected (Step S102: No), processing of this flow is ended. Many smoke detectors 234 are installed in the facility, and are set not to detect abnormality in the level of smoke (for example, smoke of a cigarette) other than smoke from a fire, so that processing of this flow is normally ended here.
Re claim 4: (Original) The method of claim 3, wherein determining that the sensor data received from the at least one sensor comprises the abnormality further comprises:
comparing the sensor data received from the at least one sensor with a historical sensor data received from the at least one sensor (e.g. the historical sensor data is no smoke being detected. This is compared to a current state of if any smoke is detected, which is taught in ¶ [95]-[97] above.); and
determining, based on the comparison, that a difference between the historical sensor data and the sensor data received from the at least one sensor exceeds a threshold difference (e.g. if the last state matches the previous state, there is no emergency. However, if the current state detects something above zero change, this is considered an emergency, which is taught in ¶ [95]-[97] above. In addition, weak smoke can be detected versus a large volume of smoke based on the comparison between a historical stored level of smoke being stored, which is taught in ¶ [97]-[100].).
[0097] In Step S102, the smoke determination means 112 (refer to FIG. 1) of the control unit 110 determines whether any one of the smoke detectors 234 has detected smoke. When no smoke is detected (Step S102: No), processing of this flow is ended. Many smoke detectors 234 are installed in the facility, and are set not to detect abnormality in the level of smoke (for example, smoke of a cigarette) other than smoke from a fire, so that processing of this flow is normally ended here.
[0098] When anyone of the smoke detectors 234 detects smoke (Step S102: Yes), in Step S103, the smoke determination means 112 of the control unit 110 determines a flow direction, volume and speed of the smoke based on facility layout information and the smoke information detected by the smoke detectors 234. In the safety-related information storage DB 135 shown in FIG. 1, installation locations of the smoke detectors 234 are stored together with the facility layout information. The control unit 110 can detect a flow direction, volume and speed of the smoke based on the installation location information of each smoke detector 234 and smoke information (whether smoke has been detected and smoke volume) from each smoke detector 234.
[0099] For example, when a certain smoke detector 234 detects smoke, the smoke determination means 112 determines smoke information of another smoke detector 234 near the smoke detector 234 that has detected smoke. By comparing smoke volumes detected by the respective smoke detectors 234, (1) a flow direction of the smoke (a direction from the smoke detector 234 that has detected a large volume of smoke to the smoke detector 234 that has detected a small volume of smoke) can be determined. Smoke spreads in all directions, however, depending on the status of a fire, the facility layout, air conditioning, and natural wind, etc., the smoke flow direction may differ. In the present embodiment, a direction of a stronger smoke flow and a direction of a comparatively weak smoke flow can be determined, so that high-quality evacuation guidance by which people are guided in a direction with a weak smoke flow can be realized.
[0100] (2) The smoke determination means 112 determines the severity of a fire, that is, a degree of urgency of evacuation from smoke volumes detected by the smoke detectors 234 (when there are many smoke detectors 234 that have detected a large volume of smoke, a degree of urgency is high). In the present embodiment, a degree of urgency of evacuation can be determined based on smoke volumes, so that evacuation guidance according to the degree of urgency can be realized. (3) A speed of smoke is determined by comparing temporal changes in smoke volume of the respective smoke detectors 234. In addition to factors similar to the directions of smoke flows described above, the speeds of smoke flows differ depending on fire preventive measures (use of fire retardants). In the present embodiment, a direction in which the flow speed of smoke is higher and a direction in which the flow speed of smoke is comparatively low can be determined, so that a high-quality evacuation guidance in that people are guided in a direction in which the flow speed of smoke is low, that is, a direction of weak fire can be realized.
Re claim 7: (Original) The method of claim 1, wherein detecting the security event comprises receiving an indication of the security event from a user (e.g. a user can press on a sensor to indicate an emergency, which is taught in ¶ [84].).
[0084] In addition, the relevant person has a pressurized safety system 310 that determines an abnormality by detecting an abdominal pressure of a person who wears the system. The pressurized safety system 310 includes an abdominal pressure sensor (not shown), etc., that detects expansion and contraction of an abdominal area of a user and outputs an expansion/contraction signal (abdominal signal), etc., and transmits an emergency signal when a user applies an abdominal pressure. An abdominal signal obtained by the abdominal pressure sensor is transmitted to the portable device 300 by a wireless communication system using Bluetooth (registered trademark).
Re claim 8: (Original) Miwa discloses the method of claim 1, wherein each type of security event is mapped to a set of attributes of objects in the database (e.g. the fire or a person who has failed to escape can be mapped to a specific location that is stored within the database about the facility, which is taught in ¶ [104]-[106] above,).
Re claim 9: (Original) Miwa discloses an apparatus for providing contextual data for a security event in an environment, comprising:
a plurality of sensors; a memory; and a processor communicatively coupled with the memory (e.g. a plurality of sensors is used to detect data within the system. The CPU and memory is used to operate the invention, which is taught in ¶ [44]-[46].) and configured to:
[0044] Hereinafter, describing “. . . means” as a subject shall mean that the control unit 110 reads out each program from a ROM as necessary and then loads it on a RAM to execute each function (described below). Each program may be stored in advance in the storage unit 130, or may be taken into the monitoring device 100 via another storage medium or communication medium when necessary.
[0045] The control unit 110 consists of a CPU (Central Processing Unit), etc., and controls the whole monitoring device 100 and executes a monitoring program to make it function as the digital smart safety system.
[0046] The control unit 110 comprises a relevant-person-position information acquisition means 111 that acquires position information of portable devices that a plurality of relevant persons respectively carry with them, a smoke determination means 112 that determines a flow direction, volume and speed of smoke based on smoke information detected by a plurality of smoke detectors 234 installed at various positions in the facility, a fire door detection means 113 that detects that a fire door/fire shutter 228 installed in the facility is not closed, a human detection means 114 that detects a person who has failed to escape in case of fire, a control means 115 that performs evacuation guidance control to designate a safe place and guide evacuation of persons relevant to the facility, and a transmission control unit 116.
receive sensor data from the plurality of sensors located in the environment, wherein the plurality of sensors comprises at least one camera and the sensor data includes at least a plurality of images captured by the at least one camera (e.g. the invention discloses several sensors that receive data within an area, wherein one of the sensors is a monitoring camera that can capture images of an area, which is taught in ¶ [62] and [69] above.);
parse the sensor data, wherein the parsing comprises:
identifying a plurality of objects in each image of the plurality of images (e.g. the invention discloses identifying a suspicious person with the use of the monitoring camera captured device. The monitoring camera can recognize a face of an individual within the image. The recognition of a body and a face are a plurality of objects, which is taught in ¶ [139]-[142] and [146] above.); and
determining attributes of each object of the plurality of objects (e.g. the system can determine what an individual is saying, where the body is located, the facial features of the user and if the user is associated with an authorized person, which is taught in ¶ [139]-[142], [145], and [150] above.);
store, in a database, the parsed sensor data comprising identifiers of the plurality of objects and the attributes (e.g. a database can store the name of a user who is permitted to use the system and enter the premises. The facial attribute of the permitted user is stored in the database with other detailed information and numbers associated with each user, which is taught in ¶ [111]-[114] and [293] above.),
wherein the database is structured such that object information is organized by timestamps and associated location in the environment (e.g. the database contains days and times when users are permitted to enter a specific area of the facility. It also identifies the users that are associated with positions in those areas, which is taught in ¶ [248], [249] and [293] above.);
detect the security event at the environment; determine a type, a time window, and a location of the security event (e.g. the invention can detect a fire or emergency event, determine the location, time frame with the use of an email and location of a nearby person to assist someone caught in the emergency event, which is taught in ¶ [216] above.);
retrieve, from the database, a set of object identifiers and a matching location of the security event (e.g. a user registered within the system and associated with a RFID tag can be detected, which is a type of identifier. The person can be detected within the permitted time to work in a specific area that matches the security database positions. This information can be retrieved within a time window of when a user is authorized to work and an email with the timestamp and location of the specific area where the emergency is occurring can be retrieved, which is taught in ¶ [216], [248], [249] and [293] above);
filter, from the set of object identifiers, at least one object identifier that matches the type of the security event (e.g. the system determines if persons are not recognized out of the stored persons within the face database (160). If the person is not recognized, the person is considered as a suspicious person. The person can also be detected by filtering images of people within a face database (160) and finding a person who performs suspicious behavior, which is taught in ¶ [112]-[114] above. This person can be detected as a matching habitual suspicious behavior.); and
output, at a computing device, the at least one object identifier and attributes of the at least one object identifier in response to detecting the security event (e.g. the system can filter out the emergency situations from a suspicious person or a person who has failed to escape. Information used to identify this emergency is gathered and put into an email with the location of the person who is stuck in the emergency in order to emergency personal to rescue the individual in peril, which is taught in ¶ [104]-[106] above.).
However, Miwa fails to specifically teach the features of retrieve, from the database, a set of object identifiers with timestamps within the time window and a matching location of the security event.
However, this is well known in the art as evidenced by Petrey. Similar to the primary reference, Petrey discloses acquiring information about people and incidents (same field of endeavor or reasonably pertinent to the problem).
Petrey discloses retrieve, from the database, a set of object identifiers with timestamps within the time window and a matching location of the security event (e.g. the invention discloses retrieving a base data set of information that is associated with an identifier of a person at the incident, the location and time of the incident that is associated with a particular incident, which is taught in ¶ [81] and [83] above. This stored information is gathered from a cloud or server to be input into a machine learning model, which is taught in ¶ [45], [46] and [61] above. The retrieving of the Id associated with a user involved in an incident, the time stamp of the incident, the type of incident and location is performed in order to be used by a machine learning model, which is taught in ¶ [90]-[93] above. A person being at a prohibited location can be considered as an incident that is notified, which is taught in ¶ [99] above.).
Therefore, in view of Petrey, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of retrieve, from the database, a set of object identifiers with timestamps within the time window and a matching location of the security event, incorporated in the device of Miwa, in order to retrieve information on individuals involved in an incident at a certain time or timeframe and location to report such persons, which aids in public safety of areas using the present technology (as stated in Petrey ¶ [26]).
Re claim 10: (Original) Miwa discloses the apparatus of claim 9, wherein an object is a person and attributes of the person include a gender, an estimated age, biometrics, clothing information, and movement history (e.g. a facial attribute can be determined, which is taught in ¶ [111]-[114] above).
Re claim 11: (Original) Miwa discloses the apparatus of claim 9, wherein detecting the security event further comprises determining that sensor data received from at least one sensor of the plurality of sensors comprises an abnormality (e.g. smoke can be detected by the smoke detectors to detect a fire, which is taught in ¶ [95]-[97] above.).
Re claim 12: (Original) Miwa discloses the apparatus of claim 11, wherein determining that the sensor data received from the at least one sensor comprises the abnormality further comprises:
comparing the sensor data received from the at least one sensor with a historical sensor data received from the at least one sensor (e.g. the historical sensor data is no smoke being detected. This is compared to a current state of if any smoke is detected, which is taught in ¶ [95]-[97] above.); and
determining, based on the comparison, that a difference between the historical sensor data and the sensor data received from the at least one sensor exceeds a threshold difference (e.g. if the last state matches the previous state, there is no emergency. However, if the current state detects something above zero change, this is considered an emergency, which is taught in ¶ [95]-[97] above. In addition, weak smoke can be detected versus a large volume of smoke based on the comparison between a historical stored level of smoke being stored, which is taught in ¶ [97]-[100] above.).
Re claim 15: (Original) Miwa discloses the apparatus of claim 9, wherein detecting the security event comprises receiving an indication of the security event from a user (e.g. a user can press on a sensor to indicate an emergency, which is taught in ¶ [84] above.).
Re claim 16: (Original) Miwa discloses the apparatus of claim 9, wherein each type of security event is mapped to a set of attributes of objects in the database (e.g. the fire or a person who has failed to escape can be mapped to a specific location that is stored within the database about the facility, which is taught in ¶ [104]-[106] above,).
Re claim 17: (Original) Miwa discloses a non-transitory computer-readable medium having stored instructions for providing contextual data for a security event in an environment, wherein the instructions are executable by a processor to:
receive sensor data from a plurality of sensors located in the environment, wherein the plurality of sensors comprises at least one camera and the sensor data includes at least a plurality of images captured by the at least one camera (e.g. the invention discloses several sensors that receive data within an area, wherein one of the sensors is a monitoring camera that can capture images of an area, which is taught in ¶ [62] and [69] above.);
parse the sensor data, wherein to parse comprises to:
identify a plurality of objects in each image of the plurality of images (e.g. the invention discloses identifying a suspicious person with the use of the monitoring camera captured device. The monitoring camera can recognize a face of an individual within the image. The recognition of a body and a face are a plurality of objects, which is taught in ¶ [139]-[142] and [146] above.); and
determine attributes of each object of the plurality of objects (e.g. the system can determine what an individual is saying, where the body is located, the facial features of the user and if the user is associated with an authorized person, which is taught in ¶ [139]-[142], [145], and [150] above.);
store, in a database, the parsed sensor data comprising identifiers of the plurality of objects and the attributes (e.g. a database can store the name of a user who is permitted to use the system and enter the premises. The facial attribute of the permitted user is stored in the database with other detailed information and numbers associated with each user, which is taught in ¶ [111]-[114] and [293] above.),
wherein the database is structured such that object information is organized by timestamps and associated location in the environment (e.g. the database contains days and times when users are permitted to enter a specific area of the facility. It also identifies the users that are associated with positions in those areas, which is taught in ¶ [248], [249] and [293] above.);
detect the security event at the environment; determine a type, a time window, and a location of the security event (e.g. the invention can detect a fire or emergency event, determine the location, time frame with the use of an email and location of a nearby person to assist someone caught in the emergency event, which is taught in ¶ [216] above.);
retrieve, from the database, a set of object identifiers with timestamps within the time window and a matching location of the security event (e.g. a user registered within the system and associated with a RFID tag can be detected, which is a type of identifier. The person can be detected within the permitted time to work in a specific area that matches the security database positions. This information can be retrieved within a time window of when a user is authorized to work and an email with the timestamp and location of the specific area where the emergency is occurring can be retrieved, which is taught in ¶ [216], [248], [249] and [293] above);
filter, from the set of object identifiers, at least one object identifier that matches the type of the security event (e.g. the system determines if persons are not recognized out of the stored persons within the face database (160). If the person is not recognized, the person is considered as a suspicious person. The person can also be detected by filtering images of people within a face database (160) and finding a person who performs suspicious behavior, which is taught in ¶ [112]-[114] above. This person can be detected as a matching habitual suspicious behavior.); and
output, at a computing device, the at least one object identifier and attributes of the at least one object identifier in response to detecting the security event (e.g. the system can filter out the emergency situations from a suspicious person or a person who has failed to escape. Information used to identify this emergency is gathered and put into an email with the location of the person who is stuck in the emergency in order to emergency personal to rescue the individual in peril, which is taught in ¶ [104]-[106] above.).
However, Miwa fails to specifically teach the features of retrieve, from the database, a set of object identifiers with timestamps within the time window and a matching location of the security event.
However, this is well known in the art as evidenced by Petrey. Similar to the primary reference, Petrey discloses acquiring information about people and incidents (same field of endeavor or reasonably pertinent to the problem).
Petrey discloses retrieve, from the database, a set of object identifiers with timestamps within the time window and a matching location of the security event (e.g. the invention discloses retrieving a base data set of information that is associated with an identifier of a person at the incident, the location and time of the incident that is associated with a particular incident, which is taught in ¶ [81] and [83] above. This stored information is gathered from a cloud or server to be input into a machine learning model, which is taught in ¶ [45], [46] and [61] above. The retrieving of the Id associated with a user involved in an incident, the time stamp of the incident, the type of incident and location is performed in order to be used by a machine learning model, which is taught in ¶ [90]-[93] above. A person being at a prohibited location can be considered as an incident that is notified, which is taught in ¶ [99] above.).
Therefore, in view of Petrey, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of retrieve, from the database, a set of object identifiers with timestamps within the time window and a matching location of the security event, incorporated in the device of Miwa, in order to retrieve information on individuals involved in an incident at a certain time or timeframe and location to report such persons, which aids in public safety of areas using the present technology (as stated in Petrey ¶ [26]).
Re claim 18: (Currently Amended) Miwa discloses the non-transitory computer-readable medium of claim 17, wherein the instructions are further executable by the processor to detect the security event by determining that sensor data received from at least one sensor of the plurality of sensors comprises an abnormality (e.g. smoke can be detected by the smoke detectors to detect a fire, which is taught in ¶ [95]-[97] above.).
Re claim 19: (New) Miwa discloses the non-transitory computer-readable medium of claim 17, wherein an object is a person and attributes of the person include a gender, an estimated age, biometrics, clothing information, and movement history (e.g. a facial attribute can be determined, which is taught in ¶ [111]-[114] above).
Re claim 20: (New) Miwa discloses the non-transitory computer-readable medium of claim 17, wherein each type of security event is mapped to a set of attributes of objects in the database (e.g. the fire or a person who has failed to escape can be mapped to a specific location that is stored within the database about the facility, which is taught in ¶ [104]-[106] above.).
Claim(s) 5, 6, 13 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Miwa, as modified by the features of Petrey, as applied to claims 1 and 9 above, and further in view of Jones (US Pub 2011/0273284).
Re claim 5: (Original) However, Miwa fails to specifically teach the features of the method of claim 4, wherein the at least one sensor comprises a temperature sensor and the abnormality is a temperature difference from an average temperature that exceeds the threshold difference.
However, this is well known in the art as evidenced by Jones. Similar to the primary reference, Jones discloses determining when to send an emergency notification (same field of endeavor or reasonably pertinent to the problem).
Jones discloses wherein the at least one sensor comprises a temperature sensor and the abnormality is a temperature difference from an average temperature that exceeds the threshold difference (e.g. the invention discloses a temperature sensor that detects a temperature exceeding above a threshold. If the threshold is exceeded and is different from the threshold, an alarm is produced, which is taught in ¶ [37].).
[0037] Another feature that may be added is a temperature sensor, such as element 11 in FIG. 1. This temperature sensor could allow the system to be activated if the temperature exceeds or falls below a threshold. Low temperature could indicate a broken furnace or loss of structure integrity during cold weather. High temperature could indicate a fire, and be used in addition to the audio monitoring to provide additional information during an emergency event. Temperature sensor 11 may be connected to the processor to allow production of a signal indicating that the temperature has moved above or below a high or low threshold.
Therefore, in view of Jones, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of wherein the at least one sensor comprises a temperature sensor and the abnormality is a temperature difference from an average temperature that exceeds the threshold difference, incorporated in the device of Miwa, in order to detect a temperature above a threshold, which can aid in notifying a user of additional information (as stated in Jones ¶ [37]).
Re claim 6: (Original) However, Miwa fails to specifically teach the features of the method of claim 4, wherein the at least one sensor comprises a microphone and the abnormality is a sound level difference from an average sound level that exceeds the threshold difference.
However, this is well known in the art as evidenced by Jones. Similar to the primary reference, Jones discloses determining when to send an emergency notification (same field of endeavor or reasonably pertinent to the problem).
Jones discloses wherein the at least one sensor comprises a microphone and the abnormality is a sound level difference from an average sound level that exceeds the threshold difference (e.g. the system detects a sound through a microphone and the level of sound that can trigger an alarm. If the sound is above a threshold, it exceeds a threshold difference of 0 and triggers an alarm. The sound trigger is explained in ¶ [17] and [18].).
[0017] This audio alert is detected by unit 8. On unit 8, a microphone 12 which continually monitors ambient sound detects the loud alarm sound. An optional sound level switch (physical or embedded in electronic logic or software) 14 may set a threshold detection level. A "switch" includes any fixed or programmable device set by the user, allowing sensitivity control. Sound detection may be set at a certain sensitivity level. Sound exceeding this threshold triggers activation of the rest of the system.
[0018] The audio signal passes through a sound level filter 16. If this signal meets or exceeds a pre-determined volume level, the signal may be sent to a tone range filter 18 to be used to distinguish or filter out tones or background noise not within the normal audio alarm frequencies (e.g., dog barking, loud music, etc.). This may all be integrated through a processor 42 (e.g., a microprocessor), or a logic controller component.
Therefore, in view of Jones, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of wherein the at least one sensor comprises a microphone and the abnormality is a sound level difference from an average sound level that exceeds the threshold difference, incorporated in the device of Miwa, in order to detect a sound above a threshold that can trigger an alert notification, which aid in alerting an emergency condition (as stated in Jones ¶ [16]).
Re claim 13: (Original) However, Miwa fails to specifically teach the features of the apparatus of claim 12, wherein the at least one sensor comprises a temperature sensor and the abnormality is a temperature difference from an average temperature that exceeds the threshold difference.
However, this is well known in the art as evidenced by Jones. Similar to the primary reference, Jones discloses determining when to send an emergency notification (same field of endeavor or reasonably pertinent to the problem).
Jones discloses wherein the at least one sensor comprises a temperature sensor and the abnormality is a temperature difference from an average temperature that exceeds the threshold difference (e.g. the invention discloses a temperature sensor that detects a temperature exceeding above a threshold. If the threshold is exceeded and is different from the threshold, an alarm is produced, which is taught in ¶ [37] above.).
Therefore, in view of Jones, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of wherein the at least one sensor comprises a temperature sensor and the abnormality is a temperature difference from an average temperature that exceeds the threshold difference, incorporated in the device of Miwa, in order to detect a temperature above a threshold, which can aid in notifying a user of additional information (as stated in Jones ¶ [37]).
Re claim 14: (Original) However, Miwa fails to specifically teach the features of the apparatus of claim 12, wherein the at least one sensor comprises a microphone and the abnormality is a sound level difference from an average sound level that exceeds the threshold difference.
However, this is well known in the art as evidenced by Jones. Similar to the primary reference, Jones discloses determining when to send an emergency notification (same field of endeavor or reasonably pertinent to the problem).
Jones discloses wherein the at least one sensor comprises a microphone and the abnormality is a sound level difference from an average sound level that exceeds the threshold difference (e.g. the system detects a sound through a microphone and the level of sound that can trigger an alarm. If the sound is above a threshold, it exceeds a threshold difference of 0 and triggers an alarm. The sound trigger is explained in ¶ [17] and [18] above.).
Therefore, in view of Jones, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of wherein the at least one sensor comprises a microphone and the abnormality is a sound level difference from an average sound level that exceeds the threshold difference, incorporated in the device of Miwa, in order to detect a sound above a threshold that can trigger an alert notification, which aid in alerting an emergency condition (as stated in Jones ¶ [16]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bapat discloses determining an emergency situation.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAD S DICKERSON whose telephone number is (571)270-1351. The examiner can normally be reached Monday-Friday 10AM-6PM EST..
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/CHAD DICKERSON/ Primary Examiner, Art Unit 2682
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